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Technology & AI Sector Trading: An OverviewKey Sub-Sectors in Technology & AI Trading Software & Services Includes companies offering software applications, SaaS (Software as a Service), enterprise solutions, cybersecurity, and IT consulting. Example: Microsoft, Adobe, Salesforce. Drivers: Cloud adoption, digital transformation, subscription-based revenue models. Hardware & Devices Encompasses manufacturers of computers, servers, networking devices, and consumer electronics. Example: Apple, Intel, Cisco. Drivers: Product launches, innovation cycles, semiconductor demand. Semiconductors & Chips Focused on designing and producing microchips essential for AI, computing, and electronics. Example: NVIDIA, AMD, TSMC. Drivers: AI adoption, global chip shortages, production innovations. Artificial Intelligence & Robotics Companies developing AI models, machine learning tools, robotics, autonomous vehicles, and automation solutions. Example: OpenAI-backed enterprises, Boston Dynamics, Alphabet’s AI division. Drivers: Advancements in deep learning, automation adoption, AI integration across industries. Cloud Computing & Data Centers Firms providing cloud infrastructure, platforms, and storage services. Example: Amazon Web Services (AWS), Google Cloud, Oracle Cloud. Drivers: Digitalization of businesses, demand for scalable computing, subscription renewals. Factors Driving Technology & AI Sector Trading Innovation Cycles and Product Launches New technology products, AI models, or software releases can create strong market reactions. For example, announcements of breakthroughs in AI chips or cloud platforms often lead to immediate price surges. Earnings Growth and Revenue Models Technology firms, especially SaaS and AI companies, often have recurring revenue models that provide predictable cash flows. Analysts focus on revenue growth, subscription metrics, and margins, which heavily influence stock valuations. Global Trends & Macro Influences Increased digitalization, AI adoption, 5G rollout, and government incentives for tech innovation fuel sector growth. Geopolitical tensions (e.g., US-China trade wars) or regulatory scrutiny on data and AI ethics can affect stock prices dramatically. Market Sentiment & Speculation Technology stocks are often driven by investor sentiment. Media hype, analyst upgrades, or social media trends can lead to exaggerated moves, creating opportunities for short-term traders. Interest Rates & Valuation Impact Many tech companies, particularly growth-oriented ones, are sensitive to interest rate changes. Higher rates reduce the present value of future earnings, impacting valuations. Conversely, low rates often lead to bullish momentum. Trading Instruments in Technology & AI Stocks & Equities Direct trading of tech stocks is the most common approach. Traders evaluate fundamentals, growth potential, technical patterns, and market news. Exchange-Traded Funds (ETFs) ETFs provide diversified exposure to the tech and AI sector. Examples include: Technology Select Sector SPDR Fund (XLK) Global X Robotics & AI ETF (BOTZ) Invesco QQQ ETF (tracking Nasdaq 100) ETFs reduce company-specific risk and allow exposure to the broader tech ecosystem. Options & Derivatives Options allow traders to leverage positions, hedge risks, or speculate on price movements. Calls are popular during bullish AI trends, while puts are used for downside protection in volatile tech markets. Futures & CFDs Technology indices futures or contract-for-difference (CFD) instruments enable trading on broader sector movements without holding individual stocks. Trading Strategies in Technology & AI Growth-Based Trading Focus on companies with high revenue and earnings growth, even if valuations are premium. Key indicators: Revenue growth rate, earnings per share (EPS) trajectory, AI product adoption metrics. Momentum Trading Leveraging price trends and market sentiment. Traders track daily volume spikes, price breakouts, or sector-wide rallies. Momentum trading is common in AI-related hype cycles. Swing Trading Capitalizes on short- to medium-term price swings. Technical analysis tools like moving averages, RSI (Relative Strength Index), and MACD (Moving Average Convergence Divergence) are widely used. Event-Driven Trading Trades based on corporate events such as product launches, AI breakthroughs, quarterly earnings, or regulatory approvals. Example: Buying NVIDIA before AI chip announcements or Tesla during autonomous driving news. Sector Rotation Traders shift capital into technology when it is expected to outperform broader markets and exit when other sectors (like industrials or energy) show better potential. Requires careful monitoring of macroeconomic indicators, Fed policies, and innovation trends. Technical Analysis in Technology & AI Trading Technical analysis plays a crucial role due to sector volatility: Support & Resistance Levels: Used to identify entry and exit points. Moving Averages (MA): 50-day and 200-day MAs highlight trend direction. Relative Strength Index (RSI): Identifies overbought or oversold conditions, useful for momentum trades. Volume Analysis: Spikes in volume can indicate strong buying or selling pressure. Chart Patterns: Flags, pennants, and head-and-shoulders patterns often precede rapid price movements in tech stocks. Risk Management in Tech & AI Trading Given the sector’s high volatility, robust risk management is critical: Position Sizing Avoid overexposure to any single stock. AI and tech stocks can swing 5–10% in a day. Stop-Loss Orders Protects against sudden negative moves, especially during earnings reports or regulatory news. Diversification Combining sub-sectors like cloud, semiconductors, and AI reduces idiosyncratic risk. Hedging with Options Traders can use protective puts or covered calls to hedge against downside risk. Monitoring Global Events AI regulations, chip shortages, and interest rate changes can cause rapid shifts. Staying informed is essential. Behavioral Considerations Trading technology and AI stocks often tests psychological resilience: FOMO (Fear of Missing Out): AI hype cycles can lead traders to chase prices without analysis. Overconfidence Bias: Traders may overestimate their ability to predict technological breakthroughs. Herd Behavior: Tech rallies often attract mass attention, creating bubbles in certain stocks. Disciplined strategies and strict adherence to risk management help avoid these pitfalls. Future Trends in Technology & AI Trading AI-Driven Market Analysis Algorithmic and AI-powered tools can analyze market sentiment, predict earnings surprises, and optimize trade timing. ESG & Ethical AI Investing Investors increasingly favor companies adhering to ethical AI standards, data privacy, and environmental sustainability. Global Expansion & Emerging Markets Emerging markets adopting AI and cloud technology provide new investment opportunities. Quantum Computing and Next-Gen Technologies As AI merges with quantum computing, investors may see exponential growth opportunities in specialized tech companies. Conclusion Technology and AI sector trading offers immense opportunities due to rapid innovation, high growth potential, and transformative impact on multiple industries. However, it comes with elevated volatility, regulatory risks, and market sentiment-driven price swings. Successful trading requires a combination of: Fundamental analysis (growth metrics, AI adoption, product pipelines) Technical analysis (trend, momentum, and pattern recognition) Risk management (position sizing, hedging, diversification) Behavioral discipline (avoiding hype-driven decisions) Traders who integrate these elements while staying informed about technological advancements and global macro trends can potentially generate substantial returns, while minimizing risk in this fast-paced sector.
NSE:HDFCBANKEducation
by GlobalWolfStreet
Advanced Technical Analysis: A Comprehensive Guide1. Principles of Advanced Technical Analysis At its core, technical analysis is based on three main principles: Price Discounts Everything: All information — news, fundamentals, market sentiment — is reflected in the price. Advanced TA accepts this as a foundation, emphasizing price action over external factors. Price Moves in Trends: Markets trend in three ways — uptrend, downtrend, and sideways. Advanced analysis focuses on identifying the start and end of these trends with precision using sophisticated tools. History Repeats Itself: Patterns, behaviors, and psychology tend to repeat due to human nature. Advanced TA uses pattern recognition and statistical methods to capitalize on these repetitive behaviors. Advanced TA combines these principles with quantitative methods and behavioral insights to increase accuracy. 2. Advanced Chart Patterns While basic patterns include head and shoulders, double tops, and triangles, advanced patterns are more nuanced: Harmonic Patterns: These patterns, like the Gartley, Butterfly, Bat, and Crab, use Fibonacci ratios to identify precise reversal zones. Unlike basic patterns, harmonic patterns offer a mathematically-defined framework for entry and exit. Elliott Wave Theory: Developed by Ralph Nelson Elliott, this theory identifies recurring waves in price movement — impulsive (trend-following) and corrective (counter-trend) waves. Advanced traders use Elliott Wave to forecast multi-timeframe trends and market cycles. Market Profile: This tool analyzes the distribution of traded volume at different price levels to identify value areas, points of control, and price acceptance zones. Market Profile is highly useful for intraday and institutional trading strategies. 3. Advanced Technical Indicators Beyond moving averages and RSI, advanced traders rely on more sophisticated indicators: Ichimoku Kinko Hyo: Often called the “one-glance indicator,” it provides support, resistance, trend direction, and momentum in one chart. The Kumo (cloud) identifies trend strength and potential reversals. Fibonacci Extensions & Retracements: Advanced traders use Fibonacci levels not just for retracements, but for projecting price targets and stop-loss levels. Confluences with other indicators improve accuracy. MACD with Histogram Divergence: While the basic MACD identifies trend and momentum, analyzing divergences between MACD and price uncovers early reversal signals. Volume-based Indicators: Tools like On-Balance Volume (OBV), Chaikin Money Flow (CMF), and Volume Price Trend (VPT) help identify accumulation or distribution phases, indicating potential breakouts or breakdowns. Adaptive Indicators: Indicators like Adaptive Moving Average (AMA) and Kaufman’s Efficiency Ratio adjust to market volatility, providing a more responsive approach than static indicators. 4. Multi-Timeframe Analysis Advanced traders rarely rely on a single timeframe. Multi-timeframe analysis involves examining multiple chart intervals — from monthly to intraday — to identify trends and align trades with higher-probability setups. Key principles include: Top-Down Approach: Start with a higher timeframe to identify the major trend, then use lower timeframes to refine entries and exits. Timeframe Confluence: Trades are stronger when multiple timeframes agree on trend direction, support/resistance, and momentum. Fractal Patterns: Price movements repeat across timeframes, allowing traders to anticipate behavior in smaller or larger scales using fractal analysis. 5. Advanced Price Action Techniques Price action analysis is the study of raw price behavior without relying heavily on indicators. Advanced techniques include: Order Flow Analysis: Examining the flow of buy and sell orders in real-time markets to understand institutional activity and anticipate price moves. Candlestick Confluence: Combining multiple candlestick patterns across higher and lower timeframes to validate reversals or continuation signals. Support/Resistance with Precision: Using historical highs/lows, pivot points, Fibonacci levels, and volume clusters to identify high-probability zones for entries and exits. Trend Exhaustion Signals: Recognizing signs of overextension, like long wicks, shrinking volume, or divergence in oscillators, to anticipate reversals. 6. Quantitative and Statistical Methods Professional technical analysis increasingly incorporates quantitative methods: Statistical Indicators: Bollinger Bands, Standard Deviation Channels, and Keltner Channels help identify volatility, mean reversion, and breakout points. Correlation Analysis: Examining how assets or indices move in relation to each other to hedge or amplify trades. Backtesting and Algorithmic Validation: Advanced traders validate strategies using historical data, Monte Carlo simulations, and statistical models to measure risk and probability of success. 7. Risk Management and Trade Psychology Advanced technical analysis is incomplete without rigorous risk management: Position Sizing: Using volatility, ATR, or percentage-based methods to determine trade size. Stop-Loss Placement: Placing stops beyond key support/resistance, volatility levels, or pattern invalidation points. Reward-to-Risk Optimization: Targeting trades with at least a 2:1 or 3:1 reward-to-risk ratio ensures long-term profitability. Psychological Discipline: Advanced traders maintain emotional control, avoid overtrading, and adhere strictly to plan-based trading. 8. Integration with Fundamental and Sentiment Analysis Though TA focuses on price, advanced practitioners often combine it with fundamental and sentiment insights: Macro Events: Interest rates, earnings, or geopolitical developments can amplify technical setups. Market Sentiment Indicators: Commitment of Traders (COT) reports, VIX index, and news sentiment can provide context to technical signals. Confluence Approach: Trades with alignment between technical setups, fundamental catalysts, and market sentiment tend to have the highest probability. 9. Algorithmic and Machine Learning Approaches Modern advanced technical analysis increasingly incorporates algorithmic trading and AI: Pattern Recognition AI: Machine learning models can detect complex chart patterns faster and more accurately than humans. Predictive Analytics: Using historical price, volume, and alternative data to predict probabilities of trend continuation or reversal. Automated Execution: Advanced traders often use bots and automated scripts to execute trades when conditions are met, reducing emotional bias and ensuring precision. 10. Key Takeaways Advanced technical analysis is more than chart reading; it is an integrated science of price, volume, momentum, and psychology. Key principles for mastery include: Understanding multi-timeframe trends. Combining advanced indicators, harmonic patterns, and Elliott Wave. Using quantitative validation and backtesting for strategy reliability. Integrating price action with institutional order flow and sentiment data. Implementing strict risk management and psychological discipline. By combining these tools, techniques, and analytical frameworks, traders can increase the probability of success, adapt to changing market conditions, and make informed decisions beyond simple guesswork. Advanced technical analysis is not about finding “guaranteed” trades but about stacking probabilities in your favor.
NSE:RELIANCEEducation
by GlobalWolfStreet
AI & Technology Sector LeadershipNavigating Innovation, Strategy, and Global Impact The Artificial Intelligence (AI) and broader technology sectors have become pivotal drivers of the global economy, reshaping industries, markets, and societies. Leadership within this domain is not simply about managing companies; it requires a visionary approach, combining technological expertise, strategic foresight, and an understanding of societal impact. Effective leadership in AI and technology is thus characterized by the ability to navigate rapid innovation, drive sustainable growth, and maintain ethical stewardship over emerging technologies. 1. The Landscape of AI & Technology The AI and technology sector is remarkably diverse, encompassing areas such as software development, cloud computing, machine learning, robotics, semiconductors, cybersecurity, and more recently, generative AI and quantum computing. The sector’s growth trajectory has been exponential, fueled by data proliferation, advances in computing power, and evolving consumer behavior. According to industry reports, AI alone is expected to contribute trillions to the global economy over the next decade, with applications ranging from autonomous vehicles and precision medicine to personalized marketing and predictive analytics. This rapid expansion places unique demands on leadership. Unlike traditional industries, technology leaders must contend with disruption as a constant, where yesterday’s innovation quickly becomes obsolete. Successful leaders are those who can anticipate trends, align their organizations with emerging opportunities, and foster a culture of continuous learning and adaptability. 2. Core Traits of Technology Sector Leaders Leadership in the AI and technology space is defined by several core traits: a. Visionary Thinking: Technology leaders must envision the future impact of their innovations. For instance, AI leaders are not merely focused on developing algorithms; they must understand how these solutions reshape industries, improve efficiency, and enhance human experiences. Visionary leadership entails strategic foresight, the ability to identify trends, and the courage to pursue transformative projects even amidst uncertainty. b. Technical Acumen: While leadership encompasses more than technical expertise, understanding the technological underpinnings of one’s business is critical. Leaders must grasp AI architectures, cloud systems, cybersecurity frameworks, or software development processes to make informed strategic decisions, allocate resources efficiently, and guide teams effectively. c. Agility and Adaptability: The pace of technological change demands leaders who can pivot quickly. Organizations led by adaptive leaders can respond to disruptive innovations, emerging competitors, and shifting regulatory landscapes. Agility also extends to workforce management, ensuring that talent development, recruitment, and reskilling initiatives keep pace with evolving technological demands. d. Ethical and Responsible Leadership: With AI and technology increasingly influencing society, ethical considerations are central to leadership. Leaders must navigate issues such as data privacy, algorithmic bias, environmental sustainability, and the societal impact of automation. Ethical stewardship enhances public trust, mitigates reputational risks, and aligns technology deployment with human-centered values. e. Collaborative and Inclusive Leadership: Innovation rarely occurs in isolation. Leaders must foster collaborative environments where cross-functional teams, diverse perspectives, and open communication drive creativity. Inclusivity in hiring, team management, and product development ensures that solutions are equitable and resonate across diverse markets. 3. Strategic Pillars of Leadership in AI & Technology a. Innovation Management: At the core of technology leadership is the ability to manage and scale innovation. This involves identifying promising research areas, funding exploratory projects, and maintaining a balance between short-term returns and long-term breakthroughs. Companies like Google, Microsoft, and Tesla exemplify how strategic investment in R&D fuels competitive advantage. b. Talent Acquisition and Development: Human capital is the lifeblood of AI and technology companies. Leaders must attract top engineers, data scientists, and researchers while fostering a culture of continuous learning. Initiatives such as hackathons, mentorship programs, and partnerships with academic institutions enable the cultivation of skills that align with future technological trends. c. Market and Competitive Strategy: Successful leaders must translate technological capability into market advantage. This includes understanding customer needs, differentiating products, and leveraging partnerships or acquisitions to expand technological capabilities. Strategic decisions in AI, for example, may involve whether to focus on enterprise applications, consumer-facing solutions, or industry-specific platforms. d. Regulatory and Policy Navigation: AI and technology sectors operate under increasing regulatory scrutiny. Leaders must proactively engage with policymakers, comply with evolving regulations, and anticipate geopolitical implications of technology deployment. Cybersecurity, data governance, and AI safety regulations require a proactive approach to risk management and corporate responsibility. 4. Case Studies in Leadership a. Sundar Pichai – Alphabet Inc.: Under Pichai’s leadership, Alphabet has maintained dominance in AI and cloud computing while expanding into new arenas such as autonomous vehicles and quantum computing. Pichai exemplifies a balance of technical understanding, visionary strategy, and global market navigation. b. Satya Nadella – Microsoft: Nadella’s tenure is a testament to transformative leadership. By pivoting Microsoft toward cloud computing, AI, and enterprise solutions, he revitalized the company’s growth trajectory. Nadella emphasized culture, collaboration, and inclusivity, demonstrating that technological leadership is inseparable from organizational culture. c. Jensen Huang – NVIDIA: Huang has led NVIDIA to become a global leader in AI hardware, leveraging GPU technology to drive advances in machine learning. His focus on innovation, market foresight, and ecosystem-building underscores the importance of aligning technological capability with strategic market positioning. 5. Challenges and Future Directions a. Rapid Technological Change: Leaders must continuously monitor emerging technologies and assess their relevance. From AI generative models to quantum computing, staying ahead of technological curves is a constant challenge. b. Ethical Dilemmas: As AI systems influence decision-making in finance, healthcare, and law enforcement, leaders face heightened scrutiny over fairness, transparency, and accountability. Navigating these ethical dilemmas is increasingly central to leadership effectiveness. c. Global Competition and Geopolitics: Technology leadership is also shaped by international dynamics. Trade restrictions, intellectual property disputes, and differing regulatory frameworks require leaders to adopt globally informed strategies. d. Workforce Evolution: Automation and AI are reshaping job roles, creating opportunities and displacing traditional functions. Leaders must manage workforce transitions, reskill employees, and foster a culture that embraces change. 6. The Role of AI in Leadership Itself Interestingly, AI is also transforming leadership practices. AI-driven analytics and predictive models enhance decision-making, optimize operations, and improve customer insights. Leaders who leverage AI for strategic foresight, risk management, and organizational efficiency gain a competitive advantage. However, reliance on AI also requires caution to avoid overdependence on algorithms at the expense of human judgment and ethical considerations. 7. Conclusion Leadership in the AI and technology sector is multidimensional, combining vision, technical expertise, ethical stewardship, and strategic agility. It is not simply about producing innovative products but shaping the trajectory of industries and societies. Leaders must navigate rapid technological change, global competition, regulatory complexities, and ethical dilemmas while fostering inclusive and innovative organizational cultures. The future of AI and technology leadership will increasingly demand a synthesis of human and artificial intelligence capabilities, where leaders not only leverage technological tools but also ensure that their applications align with societal values and global progress. Those who can balance innovation with responsibility, agility with strategy, and technical insight with ethical foresight will define the next era of technological advancement, driving growth, transformation, and sustainable impact worldwide.
NSE:RELIANCEEducation
by GlobalWolfStreet
Mastering Technical Analysis1. Understanding the Foundation of Technical Analysis Technical analysis is based on three core assumptions: Price discounts everything – All known information, including fundamentals, news, and market sentiment, is already reflected in the price. Prices move in trends – Markets tend to trend, and once a trend is established, it is more likely to continue than reverse. History tends to repeat itself – Human behavior in markets is consistent, leading to recurring patterns. Mastering technical analysis starts with internalizing these principles and learning to trust price behavior over opinions or predictions. 2. Market Structure and Price Action At the heart of technical analysis lies price action—the direct study of price movement without excessive indicators. Understanding market structure involves identifying: Higher highs and higher lows (uptrend) Lower highs and lower lows (downtrend) Sideways or range-bound markets Support and resistance levels are crucial. Support is where demand overcomes supply, while resistance is where selling pressure dominates. These levels often act as decision zones where price reacts sharply. Candlestick analysis enhances price action reading. Patterns such as doji, engulfing, hammer, and shooting star reveal shifts in market sentiment. Mastery comes from observing these candles in context—not in isolation. 3. Chart Patterns and Their Psychology Chart patterns visually represent market psychology. Some of the most powerful patterns include: Trend continuation patterns: flags, pennants, rectangles Reversal patterns: head and shoulders, double top/bottom, rounding formations Consolidation patterns: triangles and ranges Each pattern reflects a battle between buyers and sellers. For example, a head and shoulders pattern signals weakening buying pressure after repeated attempts to push price higher. Mastery lies in recognizing these patterns early and confirming them with volume and price behavior. 4. Technical Indicators: Tools, Not Crutches Indicators are mathematical calculations derived from price and volume. While useful, over-reliance can create confusion. Mastery means choosing a few complementary indicators: Trend indicators: Moving averages, ADX Momentum indicators: RSI, MACD, Stochastic Volume indicators: Volume profile, OBV Volatility indicators: Bollinger Bands, ATR For example, RSI helps identify overbought and oversold conditions, but it works best when aligned with trend direction. Indicators should confirm what price action already suggests, not contradict it. 5. Time Frame Analysis and Top-Down Approach Professional traders analyze multiple time frames. This top-down approach begins with higher time frames to identify trend direction and key levels, then moves to lower time frames for precise entries. Higher time frames show trend and structure Lower time frames show entry and exit precision This alignment reduces false signals and improves consistency. Mastery involves respecting the dominant trend while timing trades efficiently. 6. Volume and Market Participation Volume validates price movement. A breakout supported by strong volume has higher reliability than one without participation. Key volume concepts include: Volume expansion during breakouts Volume divergence during trend exhaustion Accumulation and distribution phases Understanding volume reveals whether institutions are entering or exiting positions. Master traders follow volume because it reflects real commitment, not just price fluctuations. 7. Risk Management: The Core of Mastery No technical analysis system works without solid risk management. This includes: Defining risk per trade (usually 1–2% of capital) Using stop-loss orders logically (below support or above resistance) Maintaining favorable risk-reward ratios (minimum 1:2) Mastering technical analysis is less about winning every trade and more about controlling losses. Consistency in risk management separates professionals from amateurs. 8. Trading Psychology and Discipline Even the best analysis fails without emotional control. Fear, greed, and impatience distort decision-making. Master traders develop: Discipline to follow rules Patience to wait for confirmation Emotional neutrality after wins and losses A trading journal is a powerful tool. Recording setups, emotions, and outcomes helps identify behavioral patterns and refine strategy over time. 9. Backtesting and Continuous Improvement Technical mastery requires constant refinement. Backtesting strategies on historical data builds confidence and highlights weaknesses. Markets evolve, and strategies must adapt. Learning from losses, adjusting parameters, and staying aligned with market conditions ensure long-term growth. Mastery is not a destination—it is a continuous learning process. 10. Integrating Technical Analysis with Market Context While technical analysis focuses on charts, awareness of broader market context enhances accuracy. Economic events, sector trends, and inter-market correlations influence price behavior. A technically strong setup aligned with favorable market conditions carries higher probability. Conclusion Mastering technical analysis is a blend of art and science. It requires deep understanding of price behavior, disciplined risk management, emotional control, and continuous learning. There is no perfect indicator or pattern, but there is consistency in approach. Traders who respect probability, manage risk, and stay adaptable ultimately succeed. Technical analysis is not about predicting markets—it is about preparing for them with clarity, structure, and confidence.
BSE:SENSEXEducation
by TechnicalExpress
L&T Technology has given breakout | Now it will be unstoppable!!Hello Traders and Investors, i hope you all will be doing good. I have brought an analysis on L&T Technology Services, i hope it will be beneficial to you guy's, So let's start:- As we all know Nifty IT was not performing well since last one year, but now it is accelerating,,i think this momentum will take all good fundamentally stocks to their all time highs. today also Nifty IT roaring with 611 points up(+1.97%) as of now. I already have posted analysis of Tech Mahindra and similar kind of pattern we are seeing in this L&T Technology also. Hero Also we have seen neat and clean breakout on chart. In this also earlier trend was in Downtrend then Accumulation phase and now it has given breakout of Accumulation phase so now the trend has shift ot Uptrend. We will get confirmation in some few coming sessions. Some Technical important Levels for L&T Technology Services:- Best Entry Point to go long in it will be 4050-4120. Now it is trading at 4111. Keep stop loss at 3908 Targets will be 4311/4684 Price is above 200-D EMA (Means Trend is Positive) MACD is giving bullish crossover (Means Trend is positive) Indicator and Price Action showing bullish View in this stock. Some other Important things about this Stock:- ₹ 43,383 Cr. Current Price ₹ 4,106 High / Low ₹ 4,319 / 3,076 Stock P/E 38.1 Book Value ₹ 469 Dividend Yield 1.10 % ROCE 32.8 % ROE 25.0 % Face Value ₹ 2.00 Debt ₹ 454 Cr. EPS ₹ 111 PEG Ratio 2.16 Promoter holding 73.8 % Intrinsic Value ₹ 2,622 Pledged percentage 0.00 % EVEBITDA 23.0 PROS Company is almost debt free. Company has a good return on equity (ROE) track record: 3 Years ROE 23.7% Company has been maintaining a healthy dividend payout of 38.0% CONS Stock is trading at 8.90 times its book value. Stock is Giving opportunity for long term investment also at this levels. So trade carefully and always follow discipline in trading. Disclaimer:- Please always do your own analysis or consult with your financial advisor before taking any kind of trades. Dear traders, If you like my work then do not forget to hit like and follow me, and guy's let me know what do you think about this idea in comment box, i would be love to reply all of you guy's. Thankyou.
NSE:LTTSLong
by TraderRahulPal
6
This tech hardware CMO could be the next turnaround story!NSE:OPTIEMUS Technicals: - Stock price has cleared 3 resistance zones in 3 days and broken out of a long consolidation - Stock is nearing its all time high resistance zone with strength - Stock price is showing high relative strength compared to NIFTY Fundamentals: - YoY data are bleak with low growth numbers - However quarterly financials are encouraging with growing sales, margins and earnings Business: - Company produces wearables for brands such as Noise and Harman which are well known Gen Z brands - They will commission new factory for wearables in Noida by December-end as part of its strategy to expand capacity to meet growing demand for smartwatches and truly wireless stereo (TWS) earbuds - They intend to expand into batteries, displays, and microphone contract manufacturing Source: Screener.in, Tickertape.in, LiveMint Disclaimer: Not a recommendation. Only for research and education purposes.
NSE:OPTIEMUS
by UnknownUnicorn23435789
1
SWKS BUY 03.09.2019BUY signal at 75.27 $ Skyworks Solutions Inc. designs, develops, manufactures and markets semiconductor products, including intellectual property. The Company's analog semiconductors are connecting people, places, and things, spanning a number of new and unimagined applications within the automotive, broadband, cellular infrastructure, connected home, industrial, medical, military, smartphone, tablet and wearable markets. Its geographical segments include the United States, Other Americas, China, Taiwan, South Korea, Other Asia-Pacific, Europe, Middle East and Africa. This company develops 5G technology. If you want to see more history of this strategy, I will able to show you if you request me. ATTENTION this strategy may has downtrend about 10-15%, so you can split your buy order, that you have not big downtrend. __________________________________________________________________________ You can use the signals independently or like indicator of trends together with other indicators in your trading strategy. Know that the success of your strategy that based on those signals depends from your money management and the additional conditions that you make in these strategies. You use these signals inside your strategies at your own risk. The chart shows the last trades on the product + the last signal. I have several strategies for different products, and I want to show you proof of it works on history, and you will be able to see it, when returns to that profile. Therefore, subscribe and watch for that profile. The signals rare but useful.
NASDAQ:SWKSLong
by price_and_time
Longs Initiated @ 2407, Reversal too confirmStrategy: Buy @ 2407 and add more for 2600+ target ------------------------------ TCS 0.25% was on uptrend and correcting it's uptrend after hitting 2700 resistance zone . Correction was on running flat correction which was supposed to be completed below 2100 levels. Post 2100 support reach, script was able to jump and tested 2550, We are calling it's five wave impulse move in nested manner. 2407 can provide good support level here to go long.
BSE:TCSLong
by Hittesh